Analysis of a Smartphone-Based Architecture with Multiple Mobility Sensors for Fall Detection with Supervised Learning

Santoyo-Ramón, José Antonio; Casilari, E.; Cano-García, José Manuel

doi:10.3390/s18041155

Cited by 50 publications

(40 citation statements)

References 61 publications

(77 reference statements)

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“…To resolve these concerns, a breakthrough distributed system with the development of machine learning algorithms on mobile devices has been proposed 19 . With the revolution of the mobile devices of sophisticated hardware and operating systems, a large number of fall detection systems based on the mobile devices have sprung up in recent years 10,20,21 .…”

Section: List Of Figures Vmentioning

confidence: 99%

Wearable Computing With Distributed Deep Learning Hierarchy: A Study of Fall Detection

et al. 2020

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Chapter 1. Introduction 1 Chapter 2. Literature Review 5 Chapter 3. System Overview 8 Data Acquisition and Processing 9 Distributed Hierarchical Deep Learning Chapter 4. Experiment and Results Experimental Setup Result and Analysis Chapter 5. Discussion Why to use resources on the cloud server? What are the requirements about the incoming data during the training process? Why to use paired smart insole rather than only one smart insole? Chapter 6. Conclusion Chapter 7. Suggested Future Research References iv List of Tables 4.1 The main specs of the mobile device's hardware. 4.2 The comparison experimental results of using smart insoles and wearable sensors placed on SOIs for fall detection,W-waist, H-hand, T-thigh, I-smart insole, with k-nearest neighbor (knn), support vector machines (svm), decision tree (dt), discriminant (dc),Multi-Layer Perceptron (mlp) and convolutional neural network (cnn). 4.3 The best results through the six algorithm studies. The combination represents the combination of smart Insole, smartwatch and smartphone. 4.4 Performance of two different neural networks CNN and MLP under two different parameter exchange protocols. 4.5 Running time and Running Memory usage. 5.1 The confusion matrix using CNN with only one smart insole as inputs. CM-confusion matrix, Fa-Falling, SD-Sit Down, Wa-Walking, BP-Bend to Pick up, St-Standing, Be-Bending, Si-Sitting, Ly-Lying, Sq-Squatting and SP-Squat to pick up 5.2 The confusion matrix using CNN with two smart insole as inputs. v List of Figures 3.1 This figure shows the fall detection system flowchart. The sensor data collected from the smartwatch, smartphone and smart insoles. All the data are sent to the smartphone simultaneously. The smartphone will combine all three sensor data and perform deep learning through the system. During the training process, each smartphone transmits the intermediate output and label to the cloud server which will calculate corresponding gradient-outputs ∇ n j L(w, b; x, y) and release it back to the smartphone. After finishing the training process, the smartphone will deliver the accumulated gradients to the cloud server, and the cloud server will update the consensus model according to these gradients. The updated weights will distribute to one or more smartphones which send the requests. 3.2 Overview of DHNN architecture. (a) is the traditional DNN architecture. (b) introduces HNN architecture including a single smartphone and the cloud server. And (c) illustrates DHNN architecture in which the system further extends to multiple mobile deices. 4.1 (i), (ii), (iii), (iv), (v) representing the changes of acceleration, angular velocity, and orientation on smartwatch and smartphone, as well as the moving trend of pressure center of the smart insoles when presenting five different kinds of fall movements. Plot (vi), (vii), (viii), (ix), (x) showing the changes when presenting five different ADLs. vi 4.2 The distributed hierarchical deep learning architecture used in the system evaluation. The blocks in orange and blue represen...

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Section: List Of Figures Vmentioning

confidence: 99%

Wearable Computing With Distributed Deep Learning Hierarchy: A Study of Fall Detection

et al. 2020

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“…This study was focused on young people without any impairments, but it is remarkable to say that the selection of subjects should be aligned to the goal of the system and the target population who will use it. From the related works described above 10,11,12,13,14,15,16,17,18 , we can observe that there are authors that use multimodal approaches focusing in obtaining robust fall detectors or focus on placement or performance of the classifier. Hence, they only address one or two of the design issues for fall detection.…”

Section: Modalitymentioning

confidence: 99%

“…In summary, we found multimodal fall detection related works 10,11,12 that compare the performance of different combinations of modalities. Some authors address the problem of finding the best placement of sensors 13,14,15 , or combinations of sensors 13 with several classifiers 13,15,16 with multiple sensors of the same modality and accelerometers. No work was found in literature that address placement, multimodal combinations and classifier benchmark at the same time.…”

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confidence: 99%

Design and Analysis for Fall Detection System Simplification

Martínez-Villaseñor

Ponce

2020

JoVE

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This paper presents a methodology based on multimodal sensors to configure a simple, comfortable and fast fall detection and human activity recognition system that can be easily implemented and adopted. The methodology is based on the configuration of specific types of sensors, machine-learning methods and procedures. The protocol is divided into four phases: (1) database creation (2) data analysis (3) system simplification and (4) evaluation. Using this methodology, we created a multimodal database for fall detection and human activity recognition, namely UP-Fall Detection. It comprises data samples from 17 subjects that perform 5 types of falls and 6 different simple activities, during 3 trials. All information was gathered using 5 wearable sensors (tri-axis accelerometer, gyroscope and light intensity), 1 electroencephalograph helmet, 6 infrared sensors as ambient sensors, and 2 cameras in lateral and front viewpoints. The proposed novel methodology adds some important stages to perform a deep analysis of the following design issues in order to simplify a fall detection system: a) select which sensors or combination of sensors are to be used in a simple fall detection system, b) determine the best placement of the sources of information, and c) select the most suitable machine learning classification method for fall and human activity detection and recognition. Even though some multimodal approaches reported in literature only focus on one or two of the above-mentioned issues, our methodology allows simultaneously solving these three design problems related to a human fall and activity detection and recognition system. Video Link The video component of this article can be found at https://www.jove.com/video/60361/ Some systems use sensors placed around the environment to recognize falls/activities, so people do not have to wear the sensors. However, these sensors are also limited to the places where they are deployed 8 and are sometimes difficult to install. Recently, multimodal fall detection

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“…Portable and wearable devices do not suffer from these limitations. In the latter category, two main approaches can be found: one is typically based on smartphones [9][10][11][12] and another on several kinds of wearable devices with specific hardware [13][14][15][16]. As fall detection devices, smartphones suffer from several limitations, which affect the overall performance [17].…”

Section: State Of the Artmentioning

confidence: 99%

Deep Recurrent Neural Networks for Edge Monitoring of Personal Risk and Warning Situations

Torti

Musci

Guareschi

et al. 2019

Scientific Programming

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Accidental falls are the main cause of fatal and nonfatal injuries, which typically lead to hospital admissions among elderly people. A wearable system capable of detecting unintentional falls and sending remote notifications will clearly improve the quality of the life of such subjects and also helps to reduce public health costs. In this paper, we describe an edge computing wearable system based on deep learning techniques. In particular, we give special attention to the description of the classification and communication modules, which have been developed by keeping in mind the limits in terms of computational power, memory occupancy, and power consumption of the designed wearable device. e system thus developed is capable of classifying 3Daccelerometer signals in real-time and to issue remote alerts while keeping power consumption low and improving on the present state-of-the-art solutions in the literature.

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Analysis of a Smartphone-Based Architecture with Multiple Mobility Sensors for Fall Detection with Supervised Learning

Cited by 50 publications

References 61 publications

Wearable Computing With Distributed Deep Learning Hierarchy: A Study of Fall Detection

Wearable Computing With Distributed Deep Learning Hierarchy: A Study of Fall Detection

Design and Analysis for Fall Detection System Simplification

Deep Recurrent Neural Networks for Edge Monitoring of Personal Risk and Warning Situations

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